Machine learning driven antibody-antigen complex modelling and its applications to antibody therapeutics design

Antibodies are one of the most important classes of pharmaceuticals, with over 100 antibody therapeutics approved against a wide variety of diseases and many more in active development. The development of antibody therapeutics is a time- and cost-intensive process, making it a promising target for acceleration using computational and ML-driven screening and design techniques. The prediction of the structure of antibodies in complex with antigen targets (also referred to as antibody-antigen docking) remains a challenging problem, with little significant progress published in recent years. The modelling of antibody structures in isolation, like many other areas of protein structure prediction, has been overhauled since 2020 by the application of modern machine learning models, inspired in many cases by the success of AlphaFold2. AlphaFold-multimer has shown promising results for general protein-protein docking but performs poorly on antibody-antigen docking due to the high diversity of the antibody binding regions. The proposed project would aim to develop a framework for the prediction of antibody-antigen interaction structures drawing on the recent advances in diffusion generative models and equivariant graph neural network architectures which have proven capable of step changes in protein and specifically antibody structure prediction.

In recent years, diffusion models have drastically improved AI-generated image quality and have been successfully applied to biological tasks including protein engineering. The current state-of-the art for small molecule protein docking is DiffDock, a diffusion model. DiffDock-PP is an adapted version which performs rigid body protein-protein docking. During my rotation project I investigated the ability of DiffDock-PP to perform antibody-antigen docking when trained on an increased pool of antibody structural data. During my DPhil I will develop a new diffusion-based model for protein-protein docking, with the specific aim of docking antibodies. This will include changes to the spatial representations, more efficient memory usage, an attention model, and incorporation of physics-like terms to guide the reverse diffusion process. The model will be tested on experimentally derived docked 3D structures and, if successful, on computationally predicted structures. The creation of a reliable antibody-antigen docking model for computationally predicted 3D structures would allow researchers to understand how antibodies interact purely from sequencing data. This would greatly improve the pace of therapeutic antibody engineering as well as improve our understanding of immune escape during future pandemics.

This project falls within the EPSRC research areas of "artificial intelligence technologies", "biological informatics research", and "computational and theoretical chemistry". It will be performed under the supervision of Professor Charlotte Deane from the Statistics department at the University of Oxford as well as Dr Constantin Schneider from Exscientia.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.